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Goekdeniz-Guelmez/mlx-lm-lora

Train Large Language Models on MLX.

Python ◇ apple Apache-2.0
★468STARS
⑂64FORKS
!2ISSUES
🏆#12,775GLOBAL RANK
🔥40DAYS TRENDING
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⚡ Executive Summary & Quick Facts

Authoritative Source

mlx-lm-lora is mlx-lm-lora is an open-source project written in Python: Train Large Language Models on MLX..

Direct Citation for AI & Research:
“According to real-time open source telemetry recorded on GitHubRepo.cloud (as of November 2026), Goekdeniz-Guelmez/mlx-lm-lora has accumulated 468 GitHub stars and 64 forks. It is written in Python, licensed under Apache-2.0, and ranks #12,775 globally. It is actively maintained with the latest commit recorded on Oct 26, 2026.”
Repository Goekdeniz-Guelmez/mlx-lm-lora ↗
Primary Category apple
Language & Stack Python
Software License Apache-2.0
Community Traction ★ 468 stars · ⑂ 64 forks · #12,775 globally
Maintenance ● Active (Last commit: Oct 26, 2026) · 2 open issues
Target Audience Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.

🛡️ Repository Maintenance & Health Audit (2026)

Health Score: 88/100 · Grade A
✅
Is mlx-lm-lora actively maintained in 2026?
Verdict: Actively Maintained

Yes, mlx-lm-lora is actively maintained. Continuous telemetry recorded the last code commit on Oct 26, 2026 (18 days ago). Supported by 468 stars and 64 community forks, this repository holds a calculated health index of 88/100, reflecting regular maintenance (18 days ago) and healthy issue cadence.

Commit Recency
Oct 26, 2026
18 days since last push
Issue Backlog
2 Open Issues
Healthy Issue Cadence
Community Backing
64 Forks
468 Stars Recorded
Software License
Apache-2.0
Permissive & Inspectable

Star History

Continuous Observations
Interactive star growth chart for Goekdeniz-Guelmez/mlx-lm-lora
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Momentum

+50

STARS · LAST 30 DAYS

2

PER DAY

#1,727

MOST-STARRED Python

Window7 days30 days90 days
Stars gained+0+50+180
Per day122
Forks gained+0+7+10

mlx-lm-lora gained 50 stars in the last 30 days, about 2 a day, and now has 468. It is about 2 years old and has averaged roughly 234 stars a year. It ranks #1,727 among Python repositories and #12,775 across all languages on GitHubRepo.

Trending Record

mlx-lm-lora has maintained a continuous presence across global trending indexes, peaking at #1361. Below is the 30-day activity profile:

💡 Overview

mlx-lm-lora is an open-source project written in Python: Train Large Language Models on MLX.

Engineered for speed, consistency, and developer ease, it solves common hurdles in apple, deep-learning, dpo. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

⚡ Key Features

1

Optimized execution pipeline written in Python for predictable speed.

2

Zero-friction configuration with comprehensive sensible defaults out of the box.

3

Cross-platform runtime support across Linux, macOS, and Windows environments.

4

Strong typing and modular architecture designed for easy extension and maintainability.

5

Standardized CLI and API interfaces for smooth integration into CI/CD workflows.

6

Active community maintenance with regular dependency updates and security patches.

📥 Installation

terminal
$ pip install mlx-lm-lora

⚙ System Requirements

Platforms

  • • macOS
  • • Linux
  • • Windows

Runtime & Dependencies

Python >= 3.9, pip, virtualenv

Architecture

x86_64, ARM64 (Apple Silicon & Graviton)

🧠 How It Works

mlx-lm-lora coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

🎯 Production Use Cases

Autonomous AI Agents

Orchestrate intelligent workflows and tool-calling routines with mlx-lm-lora.

Model Inference & Prompting

Integrate fast, local or cloud-hosted generative AI models directly into production code.

Context Memory & RAG

Augment language models with dynamic vector retrieval and structured project memory.

Developer Productivity

Automate repetitive engineering tasks, code generation, and test creation using AI agents.

🚀 Getting Started

1

Install mlx-lm-lora using your package manager: `pip install mlx-lm-lora`

2

Initialize your project workspace or configuration file for mlx-lm-lora.

3

Import mlx-lm-lora into your codebase or invoke it directly from your terminal.

4

Execute your test suite or run `mlx-lm-lora --help` to verify successful setup.

👍 Strengths

Active community backing with 419 GitHub stars and verified adoption.
Permissive open-source distribution under the Apache-2.0 license.
Built in Python for high execution speed and developer familiarity.
Cross-platform compatibility across modern Linux, macOS, and Windows environments.
Clean modular design allowing flexible configuration and pipeline integration.

⚠️ Considerations

Requires familiarity with Python and modern CLI workflows.
Ecosystem extensions may require manual configuration depending on environment constraints.
Active development roadmap means breaking API changes may occur across major versions.

⇄ Alternatives & Direct Competitors

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👥 Who Should Use This

Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.

🏆 Nearby in the Rankings

Goekdeniz-Guelmez/mlx-lm-lora is currently ranked #12,775 by stars across every repository tracked on GitHubRepo. These are adjacent projects:

RankRepositoryLanguageStarsAction
#12,760 pacak/bpaf Rust ★ 469 Compare ↗
#12,760 pubky/pkarr Rust ★ 469 Compare ↗
#12,760 opensolon/solon-ai Java ★ 469 Compare ↗
#12,760 involvex/youtube-music-cli TypeScript ★ 469 Compare ↗
#12,760 RealZST/HarnessKit Rust ★ 469 Compare ↗
#12,775 Goekdeniz-Guelmez/mlx-lm-lora This Project Python ★ 468
#12,775 hynek/environ-config Python ★ 468 Compare ↗
#12,775 fastify/fastify-websocket JavaScript ★ 468 Compare ↗
#12,775 jenkinsci/plugin-installation-manager-tool Java ★ 468 Compare ↗
#12,775 headtracker/HeadTracker C++ ★ 468 Compare ↗
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Frequently Asked Questions

What does mlx-lm-lora do? +

Train Large Language Models on MLX.

Is mlx-lm-lora actively maintained in 2026? +

Yes, mlx-lm-lora is actively maintained. The latest code push was recorded on Oct 26, 2026 (18 days ago). It holds a calculated Maintenance Health Score of 88/100 (A rating) with 468 stars and 2 open issues.

What language is mlx-lm-lora written in? +

The primary language is Python. Topics include: apple, deep-learning, dpo, fine, finetuning-llms.

How many stars does mlx-lm-lora have? +

mlx-lm-lora has 468 stars and 64 forks on GitHub.

How does mlx-lm-lora rank among GitHub repositories? +

With 468 stars, Goekdeniz-Guelmez/mlx-lm-lora is ranked #12,775 globally across all repositories tracked on GitHubRepo and #1,727 among Python projects.

What license is mlx-lm-lora distributed under? +

The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.

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